Name list only? Target entity disambiguation in short texts
Target entity disambiguation (TED), the task of identifying target entities of the same domain, has been recognized as a critical step in various important applications. In this paper, we propose a graphbased model called TremenRank to collectively identify target entities in short texts given a nam...
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| Main Authors: | , , , , , |
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| 格式: | text |
| 語言: | English |
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Institutional Knowledge at Singapore Management University
2015
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| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/sis_research/7470 https://ink.library.smu.edu.sg/context/sis_research/article/8473/viewcontent/D15_1077.pdf |
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| 機構: | Singapore Management University |
| 語言: | English |
| 總結: | Target entity disambiguation (TED), the task of identifying target entities of the same domain, has been recognized as a critical step in various important applications. In this paper, we propose a graphbased model called TremenRank to collectively identify target entities in short texts given a name list only. TremenRank propagates trust within the graph, allowing for an arbitrary number of target entities and texts using inverted index technology. Furthermore, we design a multi-layer directed graph to assign different trust levels to short texts for better performance. The experimental results demonstrate that our model outperforms state-of-the-art methods with an average gain of 24.8% in accuracy and 15.2% in the F1-measure on three datasets in different domains. |
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